Dynamic power throttling of spinning LIDAR

ABSTRACT

An autonomous vehicle having a LIDAR system that scans a field of view is described herein. With more specificity, a computing system of the autonomous vehicle defines a region of interest in the field of view for a scan of the field of view by the LIDAR system. The region of interest is a portion of the field of view. Based on the region of interest, the computing system transmits a control signal to the LIDAR system that causes the LIDAR system to emit first light pulses with a first intensity within the region of interest during the scan and second light pulses with a second intensity outside the region of interest during the scan. The first intensity is different from the second intensity to provide different ranges for distance measurements inside and outside the region of interest.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/586,010, filed on Sep. 27, 2019, and entitled “DYNAMIC POWERTHROTTLING OF SPINNING LIDAR”. The entirety of this application isincorporated herein by reference.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can navigate without ahuman driver. An exemplary autonomous vehicle includes a plurality ofsensor systems such as, but not limited to, a LIDAR system, a camerasystem, a global positioning system (GPS), amongst others, wherein theautonomous vehicle is controlled based upon sensor signals output by thesensor systems. The LIDAR system, in particular, can include sets oflight emitters and detectors affixed to a rotating platform, wherein thelight emitters emit light pulses into a surrounding environment as theplatform rotates. The detectors are configured to detect the lightpulses upon reflection of the light pulses from one or more objects inthe surrounding environment. Based on the detected light pulses, theLIDAR system generates a three-dimensional point cloud that isrepresentative of positions of objects in the environment surroundingthe LIDAR system.

As indicated above, an autonomous vehicle operates based uponthree-dimensional point clouds output by a LIDAR system that is mountedon or incorporated in the autonomous vehicle. Conventionally, the LIDARsystem is configured with a range (a maximum distance from the LIDARsystem at which the LIDAR system can detect objects), wherein the rangemay be a function of, for example, maximum expected velocity of theautonomous vehicle, braking distance of the autonomous vehicle, and soforth. In an example, the range may be approximately 150 meters. Toachieve this range, light emitters of the LIDAR system are driven withan amount of power that ensures that each of the light pulses emitted bythe light emitters have sufficient intensity to allow for detectors ofthe LIDAR system to detect the light pulses upon the light pulsesreflecting off of an object that is 150 meters away from the LIDARsystem.

Driving the light emitters such that this range can be achieved has beenobserved to cause the light emitters to be subject to overheating,particularly in warmer climates. In addition, as the light emitters emitlight pulses with relatively high intensities (to allow for thedetectors to detect light reflected off of objects 150 meters from theLIDAR system), light emitted by a light emitter may reflect off ofmultiple objects prior to being detected by a detector. For instance,light emitted by a light emitter may reflect off of a reflective signand then reflect off another object in the environment prior to reachingthe detector. Because the light reflected off of multiple objects priorto being detected by the detector, a three-dimensional point cloudgenerated by the LIDAR system based upon the detected light may includeinaccuracies.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims.

Described herein are various technologies pertaining to dynamic powerthrottling of light emitters of a LIDAR system. With more specificity,described herein is an autonomous vehicle comprising a LIDAR system anda computing system that controls the LIDAR system. The computing systemdefines a region of interest in a field of view of the LIDAR system,wherein the region of interest is a portion of the field of view. Thecomputing system can transmit a control signal to the LIDAR system thatcauses light emitters of the LIDAR system to emit light pulses ofdifferent intensities based upon whether the light pulses correspond tothe region of interest. That is, the LIDAR system is configured to emitfirst light pulses with a first intensity inside the region of interestduring a scan of the LIDAR system and second light pulses with a secondintensity outside of the region of interest during the scan, wherein thefirst intensity is different from the second intensity to facilitateprovision of different ranges within the scan of the LIDAR system.

The computing system may define a plurality of regions of interest inthe field of view for the scan of the LIDAR system. Accordingly, thecontrol signal transmitted to the LIDAR system may further cause thelight emitters of the LIDAR system to emit additional light pulseshaving intensities that are based upon whether the additional lightpulses correspond to the plurality of regions of interest and, in someembodiments, to which specific regions of interest the additional lightpulses correspond. More specifically, the additional light pulses areemitted from the LIDAR system with an intensity that is different fromthe intensity of the light pulses emitted outside the region ofinterest. In embodiments, light pulses emitted in the plurality ofregions of interest have the same intensity to facilitate providing asame or similar range for the plurality of regions of interest. In otherembodiments, the intensity for the additional light pulses is a thirdintensity that is different from the first intensity and the secondintensity to provide differing ranges for the plurality of regions ofinterest. In yet another embodiment, the computing system may determinethat a region of interest includes a retroreflector (e.g., a shinysurface such as a traffic sign, traffic cone, etc.) that is expected tocause a multi-path return of light pulses to the LIDAR system. In suchinstances, the control signal transmitted to the LIDAR system can beconfigured to manipulate the intensity of the light pulses emitted inthe region of interest based on a type of the retroreflector located inthe region of interest.

Prior to defining the region of interest, the computing system mayreceive geolocation data that identifies a geolocation of the autonomousvehicle, and the computing system can determine an approximate positionof a feature located in the field of view of the LIDAR system based uponthe geolocation of the autonomous vehicle. For example, the computingsystem can store a three-dimensional map of a geographic region, wherethe three-dimensional map includes locations of stationary objects.Based upon the geolocation of the autonomous vehicle (and orientation ofthe autonomous vehicle), the computing system can ascertain approximatepositions of such stationary objects relative to the autonomous vehicle,and therefore can ascertain the approximate positions of such stationaryobjects in the field of view of the LIDAR system. Thus, the computingsystem can ascertain a location of a building, a stop sign, a telephonepole, etc. relative to the autonomous vehicle. The computing system candefine the region of interest based upon the known location of an objectrelative to the autonomous vehicle, such that, for example, the regionof interest includes the object. Hence, the region of interest caninclude a stop sign, and intensity of light pulses can be controlledsuch that light pulses directed towards the stop sign have lowerintensity than light pulses directed elsewhere.

Additionally or alternatively, the computing system may receive a pointcloud output by the LIDAR system, wherein the point cloud includes acluster of points that represent an object that is not identified in thethree-dimensional map (e.g., the object is moving or is not a permanentpart of the environment). The computing system may then define theregion of interest such that the region of interest encompasses theobject, and can cause the LIDAR system to emit light pulses towards theregion of interest with an intensity that is different from light pulsesemitted by the system that are outside of the region of interest. Thecomputing system can update the region of interest over time as aposition of the object relative to the autonomous vehicle changes (e.g.,due to the autonomous vehicle changing position, the object changingposition, or both the autonomous vehicle and the object changingposition).

The computing system can define the region of interest by identifying astarting point (such as an approximate location of a centroid of anobject in a field of view of the LIDAR system), computing an elevationangle and an azimuth angle for the starting point relative to a baselineelevation angle and a baseline azimuth angle, respectively, andsubsequently forming a bounding box that encompasses an object bysetting a range of azimuth angles and elevation angles relative to thecomputed elevation angle and azimuth angle. Hence, the computing systemcan define a region of interest as having a rectangular profile, whereinthe rectangular profile surrounds the region of interest. It is to beunderstood, however, that the computing system can define a region ofinterest as having an arbitrarily-shaped enclosed profile, including anovular profile, a circular profile, a polygonal profile, a shape thatapproximately corresponds to a profile of an object in the field of viewof the LIDAR system, etc.

The technologies described herein exhibit various advantages overconventional LIDAR systems employed in autonomous vehicles. For example,light emitters of the LIDAR system need not be constantly driven withpower that causes light pulses emitted by the LIDAR system to have asame intensity. Thus, when the computing system ascertains that abuilding on the right of the autonomous vehicle is fifteen feet awayfrom the autonomous vehicle, the computing system can control the LIDARsystem such that light pulses emitted thereby have lower intensity thanlight pulses directed in the direction of travel of the autonomousvehicle, thus reducing the possibility that the LIDAR system willoverheat. In another example, the computing system can control the LIDARsystem such that light pulses directed towards a retroflector have lowerintensity than light pulses that are not directed towards aretroreflector, thereby reducing occurrences of light pulses reflectingoff multiple objects prior to being detected by the LIDAR system.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic that illustrates an autonomous vehicle having aLIDAR system that emits light pulses with intensities that are based onone or more regions of interest.

FIG. 2 illustrates and exemplary field of view of a LIDAR system intwo-dimensions,

FIG. 3 is a functional block diagram of an exemplary autonomous vehicle.

FIG. 4 is a schematic that illustrates a multi-path return of a lightpulse to a LIDAR system.

FIG. 5 is a flow diagram illustrating an exemplary methodology fordefining a region of interest in a field of view of a LIDAR system.

FIG. 6 is a flow diagram illustrating an exemplary methodology forcontrolling an intensity of light pulses emitted from a LIDAR system.

FIG. 7 illustrates an exemplary computing system.

DETAILED DESCRIPTION

Various technologies pertaining to dynamic power throttling of spinninglidar are now described with reference to the drawings, wherein likereference numerals are used to refer to like elements throughout. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofone or more aspects. It may be evident, however, that such aspect(s) maybe practiced without these specific details. In other instances,well-known structures and devices are shown in block diagram form inorder to facilitate describing one or more aspects. Further, it is to beunderstood that functionality that is described as being carried out bycertain system components may be performed by multiple components.Similarly, for instance, a component may be configured to performfunctionality that is described as being carried out by multiplecomponents.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B.

In addition, the articles “a” and “an” as used in this application andthe appended claims should generally be construed to mean “one or more”unless specified otherwise or clear from the context to be directed to asingular form.

Further, as used herein, the terms “component”, “module”, and “system”are intended to encompass computer-readable data storage that isconfigured with computer-executable instructions that cause certainfunctionality to be performed when executed by a processor. Thecomputer-executable instructions may include a routine, a function, orthe like. It is also to be understood that a component or system may belocalized on a single device or distributed across several devices.

Further, as used herein, the term “exemplary” is intended to meanserving as an illustration or example of something and is not intendedto indicate a preference.

With reference now to FIG. 1 , an exemplary environment 100 isillustrated that includes an autonomous vehicle 102 having a LIDARsystem 106 affixed thereto or incorporated therein. The autonomousvehicle 102 includes componentry depicted in call-out 104. Hence, theautonomous vehicle 102 comprises the LIDAR system 106, wherein the LIDARsystem 106 emits light pulses into the surrounding environment 100. TheLIDAR system 106 may be a spinning LIDAR system, where arrays of lightemitters and detectors spin 360 degrees when scanning an environmentsurrounding the autonomous vehicle 102. In another example, the LIDARsystem 106 may be a scanning LIDAR system, wherein the LIDAR system 106scans less than 360 degrees (e.g., 120 degrees). The autonomous vehicle102 also comprises a mechanical system 108 (e.g., a vehicle propulsionsystem, a steering system, a braking system, etc.), and a computingsystem 110. As described below, the computing system 110 is configuredto define one or more non-overlapping regions of interest (114-116) in afield of view of the LIDAR system 106 during a scan of the environment100 by the LIDAR system 106. Hence, the regions of interest 114-116 arerespective portions of the field of view of the LIDAR system 106 for ascan of the LIDAR system 106. The computing system 110 is incommunication with the LIDAR system 106 and the mechanical system 108and controls at least some operations thereof.

With more specificity, the computing system 110 includes a LIDAR controlsystem 112 that transmits a control signal to the LIDAR system 106 basedon the one or more regions of interest 114-116, wherein the controlsignal causes the LIDAR system 106 to emit first light pulses with afirst intensity in a first region of interest 114 and second lightpulses with a second intensity outside of the first region of interest114. The first intensity is different from the second intensity, suchthat the LIDAR system 106 has a first range within the first region ofinterest 114 and a second range outside the region of interest (e.g.,the first range is less than the second range). The LIDAR control system112 also transmits control signals to the mechanical system 108 based onpoint clouds received from the LIDAR system 106. A point cloud mayinclude points that represent objects, such as another vehicle 118 or aperson 120, within the one or more regions of interest 114-116 as wellas other objects, such as a building 122, within the environment 100(e.g., objects that are outside of the one or more regions of interest114-116).

In a first non-limiting embodiment, the computing system 110 may definea region of interest in the field of view of the LIDAR system 106 basedon: 1) geolocation data that identifies a geolocation of the autonomousvehicle 102; 2) an orientation of the autonomous vehicle 102; and 3) acomputer-implemented three-dimensional map of the environment 100. Forexample, the autonomous vehicle 102 can include a geolocation sensor(such as a GPS sensor) that outputs geolocation data for the autonomousvehicle 102, and the computing system 110 can receive such geolocationdata. The computing system 110 can compare the geolocation data with thethree-dimensional map to ascertain objects that are in the environmentof the autonomous vehicle 102, and can further compare an orientation ofthe autonomous vehicle 102 with the three-dimensional map to determineapproximate positions of the object relative to the autonomous vehicle102. For example, the computing system 110 can determine that thebuilding 122 is located at a certain position in the field of view ofthe LIDAR system 106. While not shown, the computing system 110, basedupon the approximate location of the building 122 relative to theautonomous vehicle 102 (and thus relative to the LIDAR system 106), candefine a region of interest that encompasses the building 122. Suchregion of interest can be rectangular in shape, ovular in shape,irregular in shape, etc. From the foregoing, it can be ascertained thatthe computing system 110 can define regions of interest that surroundpermanent (static) objects in the environment 100 as the autonomousvehicle 102 navigates the environment 100.

When defining a region of interest, the computing system 110 candetermine a desired range of the LIDAR system 112 with respect to theregion of interest. Continuing with the example set forth above, thecomputing system 110 can determine that the building 122 is 25 metersfrom the autonomous vehicle 102 and can therefore set a range of 30meters for the region of interest that surrounds the building 122.

Range of the LIDAR system 106 is a function of intensity of light pulsesthat are emitted by light emitters of the LIDAR system 106. Forinstance, when the light emitters are driven at an upper end of theiroperating range, the LIDAR system 106 may have a range of 200 meters. Inthe example set forth above, however, the building 122 is 25 meters fromthe autonomous vehicle 102, and thus the desired range of the autonomousvehicle for the region of interest that encompasses the building 122 is30 meters. Continuously driving the light emitters at the upper end oftheir operating range can cause the light emitters to overheat,particularly in hot climates. The technologies described hereinfacilitate mitigating overheating of light emitters in the LIDAR system106, as the computing system 110 defines regions of interest where lightemitters can emit light with lower intensity than other regions, suchthat the light emitters are not continuously driven at the upper end oftheir operating range. In the example set forth above, the lidar controlsystem 112, for a scan of the LIDAR system 106, causes the computingsystem 110 to transmit a control signal to the LIDAR system 106. Thecontrol signal causes the LIDAR system 106 to emit light pulses in theregion of interest surrounding the building with an intensitycorresponding to a 30 meter range (rather than a 200 meter range). Morespecifically, the LIDAR system 106, based upon the control signal,provides a first amount of current to a set of light emitters when thelight emitters emit pulses of light in the region of interest during ascan, and provides a second amount of current to the set of lightemitters when the light emitters emit pulses of light outside of theregion of interest during the scan.

In a second non-limiting embodiment, the computing system 110 can defineregions of interest that encompass dynamic objects in the environment100 (e.g., objects that are capable of moving in the environment 100 orobjects that are not permanent in the environment 100). Further, thecomputing system 110 can define regions of interest to facilitateidentifying objects in the regions of interest. For example, steam maybe rising from a steam vent in a roadway, wherein conventionally steamhas been difficult to disambiguate from some other object (such as asolid mass). In an example, for a first scan, the LIDAR system 106 canemit first light pulses towards the steam that have a first intensityand can output a first point cloud based upon the first light pulsesthat reflect off the steam and are detected by the LIDAR system 106. Thepoint cloud includes a cluster of points that represents an object thatmay be steam. The computing system 110 receives the point cloud andascertains that it is desirable to disambiguate an identity of theobject (e.g., it is desirable to determine with a relatively high levelof confidence whether the object is or is not steam). The computingsystem 110 defines a region of interest that surrounds steam in thefield of view of the LIDAR system 106 and transmits a control signal tothe LIDAR system 106. The control signal is configured to cause theLIDAR system 106 to emit second light pulses towards the steam (in theregion of interest) that have a second intensity that is different fromthe first intensity. The LIDAR system 106 outputs a second point cloudbased upon the second light pulses that reflect off the steam and aredetected by the LIDAR system 106. The computing system 110 can comparethe first point cloud with the second point cloud and based upondensities of points in the point clouds in the region of interest canascertain that the object is steam. In the case of steam or fog,densities of points corresponding to the steam or fog in two differentpoint clouds will be approximately the same despite light pulses (uponwhich the point clouds are based) being emitted from the LIDAR system106 with different intensities. Hence, the computing system 110 cancontrol the LIDAR system 106 to facilitate disambiguation between steamor fog and solid mass.

In another example, the computing system 110 can identify a dynamicobject (e.g., a person 120 or another vehicle 118) in the environment100 based on a point cloud generated by the LIDAR system 106 (and outputof other sensors of the autonomous vehicle 102). The computing system110 is configured to define the region of interest around the objectupon identifying the object in the environment 100. In an example, thecomputing system 110 can be configured to cause the LIDAR system 106 toemit light pulses in the region of interest 116 at higher intensity thanlight pulses that are directed towards the building 112 (e.g., even whenthe person 120 is closer to the autonomous vehicle 102 than the building122), such that detectors of the LIDAR system 106 may detect more lightpulses that reflect off the person 120 than would otherwise be detected.

The computing system 110 can define a region of interest for severalscans of the LIDAR system 106, wherein position of the region ofinterest in the field of view of the LIDAR system 106 changes over time.For example, with respect to the vehicle 118, the computing system 110can define the region of interest 114 as being at a first position (anddepth) in a field of view of the LIDAR system 106 for a first scan andcan define the region of interest 114 as being at a second position (anddepth) in the field of view of the LIDAR system 106 for a second scan.The computing system 110 changes the position of the region of interest114 due to relative change in position between the autonomous vehicle102 and the vehicle 118. In another example, a region of interest maytrack the curvature of the road as the autonomous vehicle 102 navigatesalong the road. Further, some features such as the curvature of a roadmay have known characteristics in which geolocation data can provide abasis for repositioning the region of interest in the field of view ofthe LIDAR system 106.

With reference now to FIG. 2 , an exemplary field of view 200 of theLIDAR system 106 with respect to a scan is illustrated. The field ofview 200 comprises a plurality of regions of interest 202-206 defined bythe computing system 110. The plurality of regions of interest 202-206may each correspond to a respective object included in the field of view200. It is to be understood that the field of view 200 is not limited tohaving a specified number of regions of interest, but rather, the numberof regions of interest defined in the field of view 200 may be basedupon a number of objects in the field of view 200. A control signaltransmitted to the LIDAR 106 system causes the LIDAR system 106 to emitfirst light pulses with a first intensity in the first region ofinterest 202 in the environment 100 during the scan, emit second lightpulses with a second intensity outside of the first region of interest202 in the environment 100 during the scan, and emit third light pulseswith a third intensity in the second region of interest 204 in theenvironment 100 during the scan, wherein the first intensity isdifferent from the second intensity and the third intensity is differentfrom the second intensity. In another example, the first intensity maybe the same as the third intensity.

Still further, the computing system 110 can define the third region ofinterest 206 in the field of view of the LIDAR system 106, wherein thecontrol signal transmitted to the LIDAR system 106 to emit fourth lightpulses with a fourth intensity in the third region of interest 206. Thefourth intensity can be different from the second intensity and can bedifferent from or the same as the first intensity and the thirdintensity. In this manner, the LIDAR system 106 is configured to emitlight pulses of different intensities for different regions of interestin a scan of the field of view 200, wherein the plurality of regions ofinterest 202-206 correspond to different ranges.

The computing system 110 can define the plurality of regions of interest202-206 through a multi-step process. With respect to the region ofinterest 202, for example, the computing system 110 can identify acenter point 208 of the first region of interest 202 that is to bedefined. The center point 208, for example, can be an estimated positionof a centroid of a first object in the field of view 200 of the LIDARsystem 106. The computing system 110 can estimate the position of thecentroid of the first object in the field of view 200 based upon ageolocation of the autonomous vehicle 102, for example, and knownlocation of the object. The object may be a traffic light, a building, areflective traffic sign, etc. In another example, when the object is nota static object, the centroid of the object in the field of view 200 canbe identified based upon: 1) one or more point clouds output by theLIDAR system 106; and 2) optionally sensor signals output by othersensor of the autonomous vehicle 102.

The computing system 110 can ascertain an elevation and azimuth for thecenter point 208, wherein the elevation and azimuth are relative to azenith 210 and fixed reference direction, respectively. The computingsystem 110 computes a range of azimuths and a range of elevations thatencompass the object in the field of view 200 (based upon a radialdistance between the LIDAR system 106 and the object), and centers suchranges at the center point 208, thereby producing a rectangular regionof interest (the region of interest 202). The computing system 110performs similar actions when defining the regions of interest 204 and206. It is to be understood, however, that the computing system 110 candefine regions of interest as having irregular shape (e.g., such thatthe region of interest has a profile that corresponds to the profile ofthe object).

With reference now to FIG. 3 , a functional block diagram of theautonomous vehicle 102 is illustrated. The autonomous vehicle 102includes the LIDAR system 106 and a plurality of sensor system 302-304.While illustrated as including one LIDAR system, it is to be understoodthat the autonomous vehicle 102 may optionally include several LIDARsystems. The sensor systems 302-304 can include a camera, a GPS sensor,an accelerometer, an inertial measurement unit, an infrared sensorsystem, a sonar sensor system, a compass, etc. The autonomous vehicle102 navigates an environment based on point clouds output by the LIDARsystems 106 and sensor signals output by the sensor systems 302-304.

The autonomous vehicle 102 further includes several mechanical systemsthat can be used to effectuate motion of the autonomous vehicle 102. Forinstance, the mechanical systems can include but are not limited to avehicle propulsion system 306, a braking system 308, and a steeringsystem 310. The vehicle propulsion system 306 may include an electricmotor, an internal combustion engine, or both. The braking system 308can include an engine brake, actuators, and/or any other suitablecomponentry that is configured to assist in decelerating the autonomousvehicle 102. The steering system 310 includes suitable componentry thatis configured to control the direction of movement of the autonomousvehicle 102.

The autonomous vehicle 102 additionally comprises the computing system110, which is in communication with the LIDAR system 106, the sensorsystems 302-304, and the mechanical systems 306-310. The computingsystem 110 comprises a data store 312 having a three-dimensional,computer-implemented map 322 stored therein, a processor 314, and memory316 that includes instructions that are executed by the processor 314.In an example, the processor 314 can be or include a graphics processingunit (GPU), a plurality of GPUs, a central processing unit (CPU), aplurality of CPUs, an application-specific integrated circuit (ASIC), amicrocontroller, a programmable logic controller (PLC), a fieldprogrammable gate array (FPGA), or the like.

Memory 316 includes the LIDAR control system 112, wherein the LIDARcontrol system 112 comprises a region of interest module 318 and anintensity module 320. The region of interest module 318, when executedby the processor 314, defines a region of interest in a field of view ofthe LIDAR system 106, as described above. The region of interest module318 defines the region of interest, in an exemplary embodiment, based onlocations of objects as identified in the map 322. Additionally oralternatively, the region of interest module 318 defines the region ofinterest based on one or more point clouds output by the LIDAR system106 and/or one or more sensor signals output by the sensor systems302-304.

The intensity module 320, when executed by the processor 314, identifiesan intensity of light pulses to be emitted in the field of view of theLIDAR system 106 and, more specifically, an intensity of light pulses tobe emitted in the region of interest defined by the region of interestmodule 318. For instance, the intensity module 320 is configured toascertain a distance between the LIDAR system 106 and the object that isencompassed by the region of interest, and determine an intensity oflight pulses to be emitted by the LIDAR system 106 in the region ofinterest based upon the distance. In another example, the intensitymodule 320 can set the intensity of light pulses in the region ofinterest based upon a type of the object encompassed by the region ofinterest. For instance, when the object is a stationary trashreceptacle, the intensity module 320 can set the intensity of lightpulses directed towards the object to be relatively low (e.g., incorrespondence to a distance between the LIDAR system 106 and theobject). In another example, when the object is a pedestrian, theintensity module 320 can set the intensity of light pulses directedtowards the object to be relatively high.

With reference now to FIG. 4 , a schematic illustrating the autonomousvehicle 102 navigating in an environment 400 where a pulse of lightemitted by the LIDAR system 106 may be subjected to a multi-path returnto the LIDAR system 106 is presented. The environment 122 includes thebuilding 122 and a reflective traffic sign 406. Conventionally, a LIDARsystem in proximity to the traffic sign 406 emits several pulses oflight towards the traffic sign 406. Rather than reflecting from thetraffic sign 406 and returning directly to the LIDAR system, one or moreof these pulses (represented by reference numeral 402) may reflect offof the traffic sign 406 towards the building 122, and then may reflectoff of the building 122 towards the LIDAR system. The one or more pulsesare able to “bounce around” in the environment 400 prior to beingdetected by the LIDAR system due to the LIDAR system emitting relativelyhigh intensity light pulses throughout an entirety of the field of viewof the LIDAR system (thereby allowing the LIDAR system to have a desiredrange in all directions). The LIDAR system may output a point cloud withinaccuracies due to the light pulse being detected after reflecting offof several objects.

The technologies described herein are configured to reduce occurrencesof multi-path returns when compared to conventional technologies. Withmore particularity, the computing system 110 is configured to define aregion of interest 404 in the field of view that encompasses the trafficsign 406. It is to be understood that the computing system 110 candefine regions of interest that encompass various types of reflectiveobjects, such as traffic cones, advertising signs, etc. The computingsystem 110 can determine a location of the traffic sign 406 relative tothe LIDAR system 106 based upon the map 322 referenced above and ageolocation and orientation of the autonomous vehicle 102 in theenvironment 400. The computing system 110 defines the region of interest404 to encompass the traffic sign 406, and further transmits a controlsignal to the LIDAR system 106 that causes the LIDAR system 106 to emitfirst light pulses having a first intensity in the region of interest406 and second light pulses having a second intensity outside of theregion of interest. In an example, the first intensity can be less thanthe second intensity. Further, the first intensity can be selected tocorrespond to a distance between the LIDAR system 106 and the trafficsign 406, wherein light pulses directed towards the region of interest404 lack sufficient intensity to “bounce around” the environment priorto being detected by the LIDAR system 106.

FIGS. 5-6 illustrate exemplary methodologies relating to definingregions of interest in a field of view of a LIDAR system and forcontrolling the LIDAR system based upon the regions of interest. Whilethe methodologies are shown and described as being a series of acts thatare performed in a sequence, it is to be understood and appreciated thatthe methodologies are not limited by the order of the sequence. Forexample, some acts can occur in a different order than what is describedherein. In addition, an act can occur concurrently with another act.Further, in some instances, not all acts may be required to implement amethodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

Referring now to FIG. 5 , an exemplary methodology 500 for defining aregion of interest in a field of view of a LIDAR system is illustrated.The methodology 500 is performed by the computing system 110 of theautonomous vehicle 102. The methodology 500 starts at 502, and at 504 alocation of an object in a field of view of a LIDAR system isidentified. The location of the object may be identified in the field ofview based upon a perception system executed by the computing system110, wherein the perception system receives sensor signals output bysensors of the autonomous vehicle and identifies objects based upon suchsensor signals. The sensors can include cameras, LIDAR systems, etc.Additionally or alternatively, the location of the object in the fieldof view may be identified based on a geolocation of the autonomousvehicle, an orientation of the autonomous vehicle, and a knowngeolocation of the object in an environment of the autonomous vehicle.In another example, the location of the object in the field of view maybe identified based upon a point cloud output by the LIDAR system and/orsensor signals output by other sensor systems of the autonomous vehicle102.

At 506, a center point for the region of interest is identified based onthe identified location of the object in the field of view of the LIDARsystem. The center point can be identified, for instance, as centroid ofthe object. Furthermore, the center point can have an azimuth andelevation. At 508, the region of interest is defined based upon thecenter point. For example, a range of azimuths and elevations thatencompasses the object in the field of view of the LIDAR system can bedetermined and centered on the center point. Additionally oralternatively, the region of interest may comprise a shape thatcorresponds to a profile of the object. It should be appreciated fromthe foregoing that determining whether to define a region of interest inthe field of view of the LIDAR system may be based on mission-specificpreferences and/or requirements of navigation. At 510, the LIDAR systemis controlled based upon the defined region of interest. The methodology500 completes at 512.

Referring now to FIG. 6 , an exemplary methodology 600 for controllingan intensity of light pulses emitted from a LIDAR system is illustrated.The methodology 600 can be performed by the computing system 110 of theautonomous vehicle 102. The methodology 600 starts at 602, and at 604 aregion of interest is defined in a field of field of view of a LIDARsystem for a scan of the field of view by the LIDAR system. The regionof interest comprises a portion of the field of view of the LIDARsystem. At 606, based upon the region of interest, a control signal istransmitted to the LIDAR system that causes the LIDAR system to emitfirst light pulses with a first intensity in the region of interestduring the scan and second light pulses with a second intensity outsidethe region of interest during the scan, such that the LIDAR system has afirst range for the region of interest and a second range outside theregion of interest. The methodology 600 completes at 608.

Referring now to FIG. 7 , a high-level illustration of an exemplarycomputing device 700 that can be used in accordance with the systems andmethodologies disclosed herein is illustrated. For instance, thecomputing device 700 may be or include the computing system 110. Thecomputing device 700 includes at least one processor 702 that executesinstructions that are stored in a memory 704. The instructions may be,for instance, instructions for implementing functionality described asbeing carried out by one or more modules discussed above or instructionsfor implementing one or more of the methods described above. Theprocessor 702 may access the memory 704 by way of a system bus 706. Inaddition to storing executable instructions, the memory 704 may alsostore geospatial data of objects, a map of an environment, etc.

The computing device 700 additionally includes a data store 708 that isaccessible by the processor 702 by way of the system bus 706. The datastore 708 may include executable instructions, geolocation data, a mapof an environment, and the like. The computing device 700 also includesan input interface 710 that allows external devices to communicate withthe computing device 700. For instance, the input interface 710 may beused to receive instructions from an external computing device, from auser, etc. The computing device 700 also includes an output interface712 that interfaces the computing device 700 with one or more externaldevices. For example, the computing device 700 may transmit controlsignals to the LIDAR system 106, the vehicle propulsion system 206, thebraking system 208, and/or the steering system 210 by way of the outputinterface 712.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 700 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 700.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and blu-ray disc (BD), where disks usually reproducedata magnetically and discs usually reproduce data optically withlasers. Further, a propagated signal is not included within the scope ofcomputer-readable storage media. Computer-readable media also includescommunication media including any medium that facilitates transfer of acomputer program from one place to another. A connection, for instance,can be a communication medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio and microwave are includedin the definition of communication medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Alternatively, or in addition, the functionally described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims. Furthermore, to the extent that theterm “includes” is used in either the details description or the claims,such term is intended to be inclusive in a manner similar to the term“comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. An autonomous vehicle, comprising: a LIDARsystem; and a computing system in communication with the LIDAR system,wherein the computing system defines a region of interest to include astationary object at a location specified in a computer-implementedthree-dimensional map of an environment, wherein the computing systemdetermines a distance between the LIDAR system and the stationary objectin the environment based on geolocation data that identifies a locationof the autonomous vehicle and the location of the stationary objectspecified in the computer-implemented three-dimensional map, and whereinthe LIDAR system is configured to perform acts comprising: receiving acontrol signal from the computing system; based upon the control signaland during a scan of the environment by the LIDAR system: emitting firstlight pulses with a first intensity in the region of interest defined bythe computing system, the region of interest being a portion of a fieldof view of the LIDAR system, the first intensity being set to control arange of the LIDAR system based on the distance between the LIDAR systemand the stationary object in the environment; emitting second lightpulses with a second intensity outside of the region of interest,wherein the first intensity is different from the second intensity; andgenerating a return based upon the emitted first light pulses and theemitted second light pulses, wherein the return indicates distancesbetween the LIDAR system and objects in the environment.
 2. Theautonomous vehicle of claim 1, wherein the geolocation data is receivedfrom a geolocation sensor.
 3. The autonomous vehicle of claim 1, whereinthe stationary object is identified in the three-dimensional map asbeing a reflective object, and further wherein the region of interest isdefined to surround the reflective object.
 4. The autonomous vehicle ofclaim 3, wherein the first intensity is lower than the second intensity.5. The autonomous vehicle of claim 1, the acts performed by the LIDARsystem further comprising: based upon the control signal and during thescan of the environment by the LIDAR system, emitting third light pulseswith a third intensity in a second region of interest defined by thecomputing system, wherein the second region of interest isnon-overlapping with the region of interest, and further wherein thethird intensity is different from the first intensity and the secondintensity.
 6. The autonomous vehicle of claim 5, wherein the computingsystem, prior to defining the second region of interest, identifies alocation of an object in a field of view of the LIDAR system, whereinthe computing system identifies the location of the object based upon apoint cloud output by the LIDAR system prior to performing the scan,wherein the computing system defines the second region of interest basedupon the location of the object in the field of view of the LIDARsystem.
 7. The autonomous vehicle of claim 6, wherein the computingsystem defines the second region of interest to surround the object inthe field of view of the LIDAR system.
 8. The autonomous vehicle ofclaim 7, wherein the third intensity is greater than the secondintensity.
 9. The autonomous vehicle of claim 6, the acts performed bythe LIDAR system further comprising: during a subsequent scan of theLIDAR system and based upon the second region of interest, emittingfourth light pulses with a fourth intensity in the second region ofinterest defined by the computing system, wherein the fourth intensityis different from the third intensity; and disambiguating whether theobject in the second region of interest is at least one of steam or fogbased on the return corresponding to the scan and a subsequent returncorresponding to the subsequent scan.
 10. The autonomous vehicle ofclaim 1, wherein the region of interest has a rectangular profile. 11.The autonomous vehicle of claim 1, wherein the region of interest has anirregular profile.
 12. The autonomous vehicle of claim 1, wherein theregion of interest is updated over time as the location of thestationary object changes relative to the location of the autonomousvehicle.
 13. A method performed by an autonomous vehicle, the methodcomprising: by a computing system of the autonomous vehicle: defining aregion of interest to include a stationary object at a locationspecified in a computer-implemented three-dimensional map of anenvironment; and determining a distance between a LIDAR system of theautonomous vehicle and the stationary object in the environment based ongeolocation data that identifies a location of the autonomous vehicleand the location of the stationary object specified in thecomputer-implemented three-dimensional map; by the LIDAR system of theautonomous vehicle, during a scan of the LIDAR system, and based upon acontrol signal received from the computing system of the autonomousvehicle: emitting first light pulses with first intensity in the regionof interest defined by the computing system of the autonomous vehicle,wherein the region of interest is a portion of a field of view of theLIDAR system, the first intensity being set to control a range of theLIDAR system based on the distance between the LIDAR system and thestationary object in the environment; and emitting second light pulseswith second intensity outside of the region of interest, wherein thefirst intensity is different from the second intensity; by the LIDARsystem, generating a return based upon the emitted first light pulsesand the emitted second light pulses, wherein the return includes valuesthat are indicative of distances between the LIDAR system and objects inthe field of view of the LIDAR system; and navigating a roadway basedupon the return generated by the LIDAR system.
 14. The method of claim13, wherein the LIDAR system is a spinning LIDAR system that has a 360degree horizontal field of view.
 15. The method of claim 13, wherein theLIDAR system is a scanning LIDAR system that has less than a 360 degreehorizontal field of view.
 16. The method of claim 13, furthercomprising: by the computing system: identifying a type of thestationary object in the field of view of the LIDAR system; and definingthe region of interest based upon the type of the stationary object inthe field of view of the LIDAR system.
 17. The method of claim 16,wherein the type of the stationary object is a reflective traffic sign.18. An autonomous vehicle comprising: a LIDAR system; and a computingsystem that is configured to perform acts comprising: defining a regionof interest to include a stationary object at a location specified in acomputer-implemented three-dimensional map of an environment; anddetermining a distance between a LIDAR system of the autonomous vehicleand the stationary object in the environment based on geolocation datathat identifies a location of the autonomous vehicle and the location ofthe stationary object specified in the computer-implementedthree-dimensional map; wherein the LIDAR system is configured to performacts comprising: during a scan of the LIDAR system, and based upon acontrol signal received from the computing system of the autonomousvehicle: emitting first light pulses with first intensity in a region ofinterest defined by the computing system of the autonomous vehicle,wherein the region of interest is a portion of a field of view of theLIDAR system, wherein the first intensity is set to control a range ofthe LIDAR system based on the distance between the LIDAR system and thestationary object in the environment; and emitting second light pulseswith second intensity outside of the region of interest, wherein thefirst intensity is different from the second intensity; generating areturn based upon the emitted first light pulses and the emitted secondlight pulses, wherein the return includes values that are indicative ofdistances between the LIDAR system and objects in the field of view ofthe LIDAR system, and further wherein the autonomous vehicle isconfigured to navigate a roadway based upon the return.
 19. Theautonomous vehicle of claim 18, wherein the LIDAR system is a spinningLIDAR system.
 20. The autonomous vehicle of claim 18, wherein the LIDARsystem is a scanning LIDAR system.